Azure Machine Learning

What it does

Azure Machine Learning is a cloud service from Microsoft for the end-to-end machine learning lifecycle. Data scientists and developers use it to prepare data, including on Apache Spark clusters, train models with automated machine learning or custom code, build prompt flow workflows for language model applications, and deploy models to managed endpoints. Inputs are datasets and model code or foundation models from a model catalog; outputs are trained models, deployed endpoints, pipelines, and monitoring data.

People typically work through Azure Machine Learning studio, a centralized workspace for the artifacts used to build and deploy models. Distinctive pieces include a feature store for sharing features across workspaces, a model catalog for fine-tuning and deploying foundation models, responsible AI tools for interpretability and fairness assessment, and MLOps support for CI/CD-style automation and model rollouts.

It suits teams already working in Azure who need governed, scalable ML operations. Using the service requires an Azure account. There is no separate charge for the Machine Learning service itself, but compute and related services such as Blob Storage, Key Vault, Container Registry, and Application Insights are billed separately. The service-level agreement is 99.9 percent uptime.

What it can do

• Prepare data on Apache Spark clusters interoperable with Microsoft Fabric
• Automated machine learning for classification, regression, vision, and natural language processing
• Model catalog to discover, fine-tune, and deploy foundation models
• Prompt flow to design, evaluate, and deploy language model workflows
• Managed endpoints for model deployment, scoring, metric logging, and safe rollouts
• Responsible AI tools for interpretability and fairness assessment with disparity metrics

Works with

• Microsoft Fabric
• Azure Blob Storage
• Azure Key Vault
• Azure Container Registry
• Azure Application Insights

Example workflows

Train and deploy a classification model

Input: a tabular dataset. Steps: create a workspace resource, run no-code automated machine learning in studio, review the best model, and deploy it to a managed endpoint. Output: a deployed scoring endpoint with logged metrics.

Build a prompt flow application

Input: a foundation model from the model catalog and sample prompts. Steps: design a prompt flow, evaluate outputs, and deploy the workflow. Output: a deployed language model workflow exposed through an endpoint.

Before you choose

• Compute charges apply whenever training or inference runs, and separate charges apply to linked Azure services such as storage and registries.
• Regional availability varies; check that your target region supports the features and machine types you need.
• Some reserved instance options are not available for certain VM series, so check series-specific pricing before committing.

Listed under

• Coding
• Data science
• Developers
• Enterprise
• Machine learning
• MLOps
• Model deployment

Get started

Create an Azure account, then get started in the studio.

1. Create an Azure account at the get-started link, using pay as you go or the free trial.
2. Open Azure Machine Learning studio and create workspace resources.
3. Choose compute machine types, such as general-purpose CPUs or GPUs, and check the pricing options before starting training.

Billing is daily, with monthly bills, and a day starts at midnight UTC. The page cites a time-limited Azure free offer of up to 30 days with a $200 credit, which is not a permanent free tier. Exact compute rates depend on machine type and region; see the pricing details page and calculator.

30-day free trial.

Source: azure.microsoft.com/en-us/pricing/details/machine-learning, checked 28 Oct 2026. Prices can change; check before you buy.

Sources for this overview

Product pages, documentation, and pricing checked 28 Oct 2026. Each source supports part of the overview; availability and prices can change.

• Azure Machine Learning - ML as a Service | Microsoft Azure ↗azure.microsoft.com
• Pricing - Azure Machine Learning | Microsoft Azure ↗azure.microsoft.com

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knowledge_base

Last checked 28 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.